Method for mdas server assisted handover optimization in wireless network

ABSTRACT

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. Accordingly, the embodiments herein provide a method for Management data analytic service (MDAS) server ( 100 ) assisted handover optimization in a wireless network ( 1000 ). The method includes periodically collecting data from a plurality of target gNBs in the wireless network ( 1000 ), and generating an analytical report for each target gNB ( 300 ) of the plurality of target gNBs based on the collected data. Further, the method includes receiving a request for the analytical report of the at least one target gNB ( 300 ) for handover from a source gNB ( 200 ), sending the analytical report to the source gNB ( 200 ). Further, the includes performing at least one corrective action suggested by the analytical report to optimize at least one target gNB ( 300 ) for handover.

TECHNICAL FIELD

The present invention relates to a wireless network, and morespecifically related to a method for Management data analytic service(MDAS) server assisted handover optimization in the wireless network.The present application is based on, and claims priority from an IndianApplication Number 201941044137 filed on 31 Oct. 2019 the disclosure ofwhich is hereby incorporated by reference herein.

BACKGROUND ART

To meet the demand for wireless data traffic having increased sincedeployment of 4G communication systems, efforts have been made todevelop an improved 5G or pre-5G communication system. Therefore, the 5Gor pre-5G communication system is also called a ‘Beyond 4G Network’ or a‘Post LTE System’. The 5G communication system is considered to beimplemented in higher frequency (mmWave) bands, e.g., 60 GHz bands, soas to accomplish higher data rates. To decrease propagation loss of theradio waves and increase the transmission distance, the beamforming,massive multiple-input multiple-output (MIMO), Full Dimensional MIMO(FD-MIMO), array antenna, an analog beam forming, large scale antennatechniques are discussed in 5G communication systems. In addition, in 5Gcommunication systems, development for system network improvement isunder way based on advanced small cells, cloud Radio Access Networks(RANs), ultra-dense networks, device-to-device (D2D) communication,wireless backhaul, moving network, cooperative communication,Coordinated Multi-Points (CoMP), reception-end interference cancellationand the like. In the 5G system, Hybrid FSK and QAM Modulation (FQAM) andsliding window superposition coding (SWSC) as an advanced codingmodulation (ACM), and filter bank multi carrier (FBMC), non-orthogonalmultiple access (NOMA), and sparse code multiple access (SCMA) as anadvanced access technology have been developed.

The Internet, which is a human centered connectivity network wherehumans generate and consume information, is now evolving to the Internetof Things (IoT) where distributed entities, such as things, exchange andprocess information without human intervention. The Internet ofEverything (IoE), which is a combination of the IoT technology and theBig Data processing technology through connection with a cloud server,has emerged. As technology elements, such as “sensing technology”,“wired/wireless communication and network infrastructure”, “serviceinterface technology”, and “Security technology” have been demanded forIoT implementation, a sensor network, a Machine-to-Machine (M2M)communication, Machine Type Communication (MTC), and so forth have beenrecently researched. Such an IoT environment may provide intelligentInternet technology services that create a new value to human life bycollecting and analyzing data generated among connected things. IoT maybe applied to a variety of fields including smart home, smart building,smart city, smart car or connected cars, smart grid, health care, smartappliances and advanced medical services through convergence andcombination between existing Information Technology (IT) and variousindustrial applications.

In line with this, various attempts have been made to apply 5Gcommunication systems to IoT networks. For example, technologies such asa sensor network, Machine Type Communication (MTC), andMachine-to-Machine (M2M) communication may be implemented bybeamforming, MIMO, and array antennas. Application of a cloud RadioAccess Network (RAN) as the above-described Big Data processingtechnology may also be considered to be as an example of convergencebetween the 5G technology and the IoT technology.

In current Radio Access Network (RAN) handover specification adopts aprocess in which a gNodeB (gNB) (e.g. target gNB) accepts or reject ahandover request, fora user equipment (UE), from source gNB. Thehandover request may be rejected due to inadequacy of available virtualresources and radio resources associated with the target gNB of awireless network. The UE will try to connect to a different gNB of thewireless network until the handover request is successfully accepted byat least one gNB of the wireless network. This hit-and-trial mechanismresults in wastage of UE resources and wireless network resources.Existing handover mechanisms can be optimized using a Management dataanalytic service (MDAS) server and minimizing chances for a probablehandover failure.

Thus, it is desired to address the above-mentioned disadvantages orother shortcomings or at least provide a useful alternative.

DISCLOSURE OF INVENTION Technical Problem

The principal object of the embodiments herein is to provide a methodfor Management data analytic service (MDAS) server assisted handoveroptimization in a wireless network.

Another object of the embodiments is to periodically collect data from aplurality of target gNBs in the wireless network and generate ananalytical report for each target gNB of the plurality of target gNBsbased on the collected data, where the analytical report comprises atleast one a current resource consumption status for each of the targetgNBs, and a future resource consumption status for each of the targetgNBs.

Another object of the embodiments is to receive a request for theanalytical report of the at least one target gNB for handover from asource gNB from the plurality of target gNBs and based on the analyticalreport the source gNB determines whether the target gNB is optimal forhandover at present (current optimal target) or at some future point oftime (future optimal target) before initiating handover procedure tosave UE resources.

Another object of the embodiments is to provide recommended actions whenthe target gNB is not optimal for handover, the analytical reportprovides recommended actions to optimize the target gNB for handover(i.e. the source gNB will take measures (indicated by an MDAS server) tomake the target gNB optimal (e.g. by asking Network functionsvirtualization (NFV) Management and Orchestration (MANO) to scale-outthe target gNB)).

Solution to Problem

Accordingly, the embodiments herein provide a method for MDAS serverassisted handover optimization in a wireless network. The methodincludes periodically collecting, by an MDAS server (i.e. Managementdata analytic service producer), data from a plurality of target gNBs inthe wireless network. Further, the method includes generating, by theMDAS server, an analytical report for each target gNB of the pluralityof target gNBs based on the collected data, where the analytical reportcomprises at least one a current resource consumption status for each ofthe target gNBs, a future resource consumption status for each of thetarget gNBs, an indication on whether at least one target gNB from theplurality of target gNBs is optimal for handover at present (currentoptimal target) or at some future point of time (future optimal target)and at least one corrective action to optimize at least one target gNBfor handover. Further, the method includes receiving, by the MDASserver, a request for the analytical report of the at least one targetgNB for handover from a source gNB from the plurality of target gNBs.Further, the method includes sending, by the MDAS server, the analyticalreport to the source gNB.

In an embodiment, the collected data comprises a total amount ofcomputed resource allocated to a virtual machine, a total amount ofaggregated computed resource consumption at a particular point, a totalamount of storage allocated to the virtual machine, a total amount ofaggregated storage consumption at a particular point of time, andvarious radio resources and percentage of overall consumption of thevarious radio resources.

In an embodiment, the analytical report comprises an assigned virtualresource and radio resource of each of the target gNBs, a consumedvirtual resource and radio resource of each of the target gNBs, aprojected virtual resource and radio resource usage in near future foreach of the target gNBs, an indication on whether at least one targetgNB from the plurality of target gNBs is optimal for handover, at leastone of in present and in future (with time stamp) and at least onecorrective action to optimize each of the target gNBs for handover.

Further, the method includes receiving, by the source gNB, theanalytical report of the at least one target gNB from the MDAS server.Further, the method includes performing, by the source gNB, the at leastone corrective action to optimize at least one target gNB for handover.Further, the method includes determining, by the source gNB, whether atleast one target gNB from the plurality of target gNBs is optimal forhandover at present or at some future point of time. Further, the methodincludes performing, by the source gNB, the handover from the source gNBto the at least one target gNB in response to determining that the atleast one target gNB from the plurality of target gNBs is optimal forhandover at present or at some future point of time. Further, the methodincludes sending, by the source gNB, a scale-out request to an NFMSserver (i.e. Network Function Management Service producer) to increasevirtual resources and radio resources associated with the at least onetarget gNB in response to determining that the at least one target gNBfrom the plurality of target gNBs is not optimal for handover.

In an embodiment, sending the scale-out request to the NFMS server,further includes allocating, by the NFMS server, additional virtualresources and radio resources to the at least one target gNB. Further,the scale-out request includes informing, by the NFMS server, successfulscale-out of the at least one target gNB to the source gNB. Further, thescale-out request includes determining, by the source gNB, whether auser equipment (UE) is moving away from the target gNB in the wirelessnetwork.

In an embodiment, determining, by the source gNB, whether the UE ismoving away from the target gNB in the wireless network, includesperforming, by the source gNB, the handover from the source gNB to theat least one target gNB in response to determining that the UE is notmoving away from the target gNB in the wireless network, and sending, bythe source gNB, a scale-in request to the NFMS server to decreasevirtual resources and radio resources associated with the at least onetarget gNB in response to determining that the UE is moving away fromthe target gNB in the wireless network.

In an embodiment, sending the scale-out request to the NFMS server,further includes de-allocating, by the NFMS server, additional virtualresources and radio resources to the at least one target gNB. Further,the scale-in request includes informing, by the NFMS server, successfulscale-in of the at least one target gNB to the source gNB. The radioresources decrease in the same proportion as they were increase.

In an embodiment, the MDAS server provides the analytical reportdescribing the resource consumption to the source gNB (e.g. authorizedconsumer) based on the current and future virtual resource consumptionof the at least one target gNB.

In an embodiment, the MDAS server provides the analytical reportdescribing the resource consumption to the authorized consumer based onthe current and future radio resource consumption of the at least onetarget gNB.

In an embodiment, the MDAS server generating the analytical report usingat least one of an Artificial Intelligence (AI) model and a MachineLearning (ML) model.

Accordingly, the embodiments herein provide a method for MDAS serverassisted handover optimization in a wireless network, the wirelessnetwork comprises a plurality of gNBs. The method includes detecting, bya source gNB from the plurality of gNBs that a UE moves from at leastone target gNB from a plurality of target gNBs in the wireless network.Further, the method includes sending, by the source gNB, a request to anMDAS server for an analytical report of the at least one target gNB forhandover from the source gNB, where the analytical report comprises atleast one a current resource consumption status for each of the targetgNBs, a future resource consumption status for each of the target gNBs.Further, the method includes an indication on whether at least onetarget gNB from the plurality of target gNBs is optimal for handover atpresent or at some future point of time and at least one correctiveaction to optimize at least one target gNB for handover. Further, themethod includes receiving, by the source gNB, the analytical report ofthe at least one target gNB from the MDAS server. Further, the methodincludes performing, by the source gNB, at least one corrective actionto optimize at least one target gNB for handover.

Accordingly, the embodiments herein provide the MDAS server forproviding an optimal handover in the wireless network. The MDAS serverincludes an analytical report generator coupled with a processor and amemory. The analytical report generator is configured to periodicallycollect data from a plurality of target gNBs in the wireless network.Further, the analytical report generator is configured to generate ananalytical report for each target gNB of the plurality of target gNBsbased on the collected data. Further, the analytical report generator isconfigured to receive a request for the analytical report of the atleast one target gNB for handover from a source gNB from the pluralityof target gNBs. Further, the analytical report generator is configuredto send the analytical report to the source gNB.

Accordingly, the embodiments herein provide the source gNB for providingan optimal handover in the wireless network. The source gNB includes acorrective action controller coupled with a processor and a memory. Thecorrective action controller is configured to detect that a userequipment (UEL) moves from at least one target gNB from the plurality oftarget gNBs in the wireless network. Further, the corrective actioncontroller is configured to send a request to an MDAS server for ananalytical report of the at least one target gNB for handover from thesource gNB. Further, the corrective action controller is configured toreceive the analytical report of the at least one target gNB from theMDAS server. Further, the corrective action controller is configured toperform at least one corrective action to optimize at least one targetgNB for handover.

These and other aspects of the embodiments herein will be betterappreciated and understood when considered in conjunction with thefollowing description and the accompanying drawings. It should beunderstood, however, that the following descriptions, while indicatingpreferred embodiments and numerous specific details thereof, are givenby way of illustration and not of limitation. Many changes andmodifications may be made within the scope of the embodiments hereinwithout departing from the spirit thereof, and the embodiments hereininclude all such modifications.

Advantageous Effects of Invention

The principal object of the embodiments herein is to provide a methodfor Management data analytic service (MDAS) server assisted handoveroptimization in a wireless network.

BRIEF DESCRIPTION OF DRAWINGS

This method and system is illustrated in the accompanying drawings,throughout which like reference letters indicate corresponding parts inthe various figures. The embodiments herein will be better understoodfrom the following description with reference to the drawings, in which:

FIG. 1 is an overall architecture of a MDAS server assisted handoveroptimization in a wireless network, according to the embodiments asdisclosed herein;

FIG. 2A illustrates a block diagram of the MDAS server for providing anoptimal handover in a wireless network, according to the embodiments asdisclosed herein;

FIG. 2B illustrates a block diagram of a source gNB for providing anoptimal handover in the wireless network, according to the embodimentsas disclosed herein;

FIG. 3A is a flow diagram illustrating various operations for the MDASserver assisted handover optimization in the wireless network, accordingto the embodiments as disclosed herein;

FIG. 3B is a flow diagram illustrating various operations for the MDASserver assisted handover optimization in the wireless network, accordingto the embodiments as disclosed herein;

FIG. 3C is a flow diagram illustrating various operations for the MDASserver assisted handover optimization in the wireless network, accordingto the embodiments as disclosed herein;

FIG. 4A is a sequential diagram illustrating the MDAS server assistedhandover optimization in the wireless network, according to theembodiments as disclosed herein; and

FIG. 4B is a sequential diagram illustrating the MDAS server assistedhandover optimization in the wireless network, according to theembodiments as disclosed herein.

MODE FOR THE INVENTION

The embodiments herein and the various features and advantageous detailsthereof are explained more fully with reference to the non-limitingembodiments that are illustrated in the accompanying drawings anddetailed in the following description. Descriptions of well-knowncomponents and processing techniques are omitted so as to notunnecessarily obscure the embodiments herein. Also, the variousembodiments described herein are not necessarily mutually exclusive, assome embodiments can be combined with one or more other embodiments toform new embodiments. The term “or” as used herein, refers to anon-exclusive or, unless otherwise indicated. The examples used hereinare intended merely to facilitate an understanding of ways in which theembodiments herein can be practiced and to further enable those skilledin the art to practice the embodiments herein. Accordingly, the examplesshould not be construed as limiting the scope of the embodiments herein.

As is traditional in the field, embodiments may be described andillustrated in terms of blocks which carry out a described function orfunctions. These blocks, which may be referred to herein as managers,units, modules, hardware components or the like, are physicallyimplemented by analog and/or digital circuits such as logic gates,integrated circuits, microprocessors, microcontrollers, memory circuits,passive electronic components, active electronic components, opticalcomponents, hardwired circuits and the like, and may optionally bedriven by firmware. The circuits may, for example, be embodied in one ormore semiconductor chips, or on substrate supports such as printedcircuit boards and the like. The circuits constituting a block may beimplemented by dedicated hardware, or by a processor (e.g., one or moreprogrammed microprocessors and associated circuitry), or by acombination of dedicated hardware to perform some functions of the blockand a processor to perform other functions of the block. Each block ofthe embodiments may be physically separated into two or more interactingand discrete blocks without departing from the scope of the disclosure.Likewise, the blocks of the embodiments may be physically combined intomore complex blocks without departing from the scope of the disclosure.

Accordingly, the embodiments herein provide a method for Management dataanalytic service (MDAS) server assisted handover optimization in awireless network. The method is desirable to use an MDAS server toprovision/select a particular gNB for a handover to avoid handoverrejections. The MDAS server can collect several resource-specific (e.g.virtual resource, RAN resource) data from a plurality of gNB(s)periodically, analyze the data and then create a comprehensiveanalytical report for the gNB(s) providing current resource status for atarget gNB. The analytical report also indicated whether the target gNBis optimal for handover at present (current optimal target) or at somefuture point of time (future optimal target). If the target gNB is notoptimal for handover, the analytical report provides recommended actionsto optimize the target gNB for the handover. A source gNB, acting as anMDAS consumer, can request for the analytical report before executinghandover. If the target gNB is not optimal for handover, the MDASconsumer may choose to take/perform the corrective measures as suggestedin the analytical report before continuing with the handover procedures.Further, the analytical report also includes future/expected resourceconsumption information for the target gNB/gNB(s) at a future point oftime. The MDAS consumer may choose either not to proceed with thehandover if the report is suggesting a near future resource deprivationat the target gNB or take corrective actions to optimize the target gNBfor handover.

Referring now to the drawings, and more particularly to FIGS. 1 through4, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments.

FIG. 1 is an overall architecture of a MDAS server (100) assistedhandover optimization in a wireless network (1000), according to theembodiments as disclosed herein. The wireless network (1000) includesthe MDAS server (100), a source gNB (200), and a plurality of targetgNBs (at least one target gNB (300)), a UE (400), and a NFMS server(500).

At S1, the MDAS server (100) periodically collects data from theplurality of gNBs (i.e. the target gNB (300) (e.g. a first target gNB(300 a), and a second target gNB (300 b)) in the wireless network(1000). Details about the data is given in the Table. 1.

TABLE 1 Data Description Compute This describes the number of vCPUs(Openstack Nova) allocated allocated to the virtual machine on which thegNB VNF is hosted. Compute This describes the number of total aggregatedcompute consumed resource (vCPU) consumption at a particular point oftime. Storage This describes the number of vStorage (Openstack Cinder)allocated allocated to the virtual machine on which the gNB VNF ishosted. Storage This describes the number of total aggregated storageconsumed consumption at a particular point of time. Radio This describesvarious radio resource and their overall Resource consumptionpercentage. consumed

At S2, the MDAS server (100) generates the analytical report for eachtarget gNB (300) of the plurality of target gNBs (300 a, 300 b) based onthe collected data. The data is then analyzed using various AI mechanismto ascertain whether the first target gNB (300 a) is deprived ofresources or not. In case, the first target gNB (300 a) is deprived ofresources the report provides recommended actions e.g. in case ofvirtual resource deprivation, the scale-out/up will be suggested. TheMDAS server (100) has a capability allowing the source gNB (200)(authorized consumer) to get the analytical report describing theresource consumption for each gNB (300 a, 300 b). Further, the MDASserver (100) can provide the analytical report describing the resourceconsumption to the authorized consumer based on the current and futurevirtual resource consumption of each target gNB (300 a, 300 b). Further,the MDAS server (100) can provide the analytical report describing theresource consumption to the authorized consumer based on the current andfuture RAN resource consumption of each target gNB (300 a, 300 b).Further, the analytical report describing resource consumption shouldcontain the following information describing the current and futureresource consumption,

Assigned virtual resources and radio resources.

Consumed virtual resources and radio resources.

Projected virtual resources and radio resources usage in near future.

Indication on whether the target gNB is optimal for handover at present(current optimal target) or at some future point of time (future optimaltarget).

Recommended action to optimize the gNB for handover.

At S3, the source gNB (200) detects that the UE (400) moves towards atleast one target gNB (300) (i.e. the first target gNB (300 a)) from theplurality of target gNBs in the wireless network (1000). At S4, the MDASserver (100) receives a request for the analytical report of the atleast one target gNB (300) (i.e. the first gNB (300 a)) for handoverfrom the source gNB (200) from the plurality of target gNBs. At S5, theMDAS server (100) sends the analytical report to the source gNB (200).At S6, the source gNB (200) (MDAS consumer) adjusts (e.g. scale-out/up)the resources before continuing with the handover.

FIG. 2A illustrates a block diagram of the MDAS server (100) forproviding an optimal handover in the wireless network (1000), accordingto the embodiments as disclosed herein. In an embodiment, the MDASserver (100) includes a memory (110), a processor (120), a communicator(130), and an analytical report controller (140).

The memory (110) also stores instructions to be executed by theprocessor (120). The memory (110) may include non-volatile storageelements. Examples of such nonvolatile storage elements may includemagnetic hard discs, optical discs, floppy discs, flash memories, orforms of electrically programmable memories (EPROM) or electricallyerasable and programmable (EEPROM) memories. In addition, the memory(110) may, in some examples, be considered a non-transitory storagemedium. The term “non-transitory” may indicate that the storage mediumis not embodied in a carrier wave or a propagated signal. However, theterm “non-transitory” should not be interpreted that the memory (110) isnon-movable. In certain examples, a nontransitory storage medium maystore data that can, over time, change (e.g., in Random Access Memory(RAM) or cache). In an embodiment, the memory (110) can be an internalstorage unit or it can be an external storage unit of the MDAS server(100), a cloud storage, or any other type of external storage.

The processor (120) communicates with the memory (110), the communicator(130), and the analytical report controller (140). The processor (120)is configured to execute instructions stored in the memory (110) and toperform various processes. The communicator (130) is configured forcommunicating internally between internal hardware components and withexternal devices via one or more networks.

In an embodiment, the analytical report controller (140) includes a datacollector (140 a), and an AI engine (140 b). The data collector (140 a)periodically collects data from the plurality of gNBs (e.g. the targetgNB (300)) in the wireless network (1000). Further, the analyticalreport controller (140) generates an analytical report for each targetgNB (300) of the plurality of target gNBs based on the collected data,where the analytical report comprises at least one a current resourceconsumption status for each of the target gNBs, a future resourceconsumption status for each of the target gNBs (300), an indication onwhether at least one target gNB (300) (e.g. the first target node (300a), the second target node (300 b), etc.) from the plurality of targetgNBs is optimal for handover at present (current optimal target) or atsome future point of time (future optimal target) and at least onecorrective action to optimize at least one target gNB (300) forhandover. Further, the analytical report controller (140) receives arequest for the analytical report of the at least one target gNB (300)for handover from a source gNB (200). Further, the analytical reportcontroller (140) sends the analytical report to the source gNB (200).

In an embodiment, the collected data comprises a total amount ofcomputed resource allocated to a virtual machine, a total amount ofaggregated computed resource consumption at a particular point, a totalamount of storage allocated to the virtual machine, a total amount ofaggregated storage consumption at a particular point of time, andvarious radio resources and percentage of overall consumption of thevarious radio resources.

In an embodiment, the current resource consumption status for each ofthe target gNBs and the future resource consumption status for each ofthe target gNBs comprises an assigned virtual resource and radioresource of each of the target gNBs, a consumed virtual resource andradio resource of each of the target gNBs, a projected virtual resourceand radio resource usage in near future for each of the target gNBs, anindication on whether at least one target gNB (300) from the pluralityof target gNBs is optimal for handover at present (current optimaltarget) or at some future point of time (future optimal target), and atleast one corrective action to optimize each of the target gNBs forhandover.

In an embodiment, the analytical report controller (140) provides theanalytical report describing the resource consumption to the authorizedconsumer based on the current and future virtual resource consumption ofthe at least one target gNB (300).

In an embodiment, the analytical report controller (140) provides theanalytical report describing the resource consumption to the authorizedconsumer based on the current and future radio resource consumption ofthe at least one target gNB (300).

In an embodiment, the analytical report controller (140) generates theanalytical report using at least one of an Artificial Intelligence (AI)model and a Machine Learning (ML) model. The AI engine (140 b) utilizingcollected data and AI/ML (for example, time series based) algorithm toderive the future handover optimality.

At least one of the plurality of modules/components may be implementedthrough an AI model. A function associated with AI may be performedthrough memory (110) and the processor (120).

The processor (120) may include one or a plurality of processors. Atthis time, one or a plurality of processors may be a general-purposeprocessor, such as a central processing unit (CPU), an applicationprocessor (AP), or the like, a graphics-only processing unit such as agraphics processing unit (GPU), a visual processing unit (VPU), and/oran AI-dedicated processor such as a neural processing unit (NPU).

The one or a plurality of processors controls the processing of theinput data in accordance with a predefined operating rule or artificialintelligence (AI) model stored in the non-volatile memory and thevolatile memory. The predefined operating rule or artificialintelligence model is provided through training or learning.

Here, being provided through learning means that, by applying a learningprocess to a plurality of learning data, a predefined operating rule orAI model of a desired characteristic is made. The learning may beperformed in a device itself in which AI according to an embodiment isperformed, and/o may be implemented through a separate server/system.

The AI model may consist of a plurality of neural network layers. Eachlayer has a plurality of weight values and performs a layer operationthrough calculation of a previous layer and an operation of a pluralityof weights. Examples of neural networks include, but are not limited to,convolutional neural network (CNN), deep neural network (DNN), recurrentneural network (RNN), restricted Boltzmann Machine (RBM), deep beliefnetwork (DBN), bidirectional recurrent deep neural network (BRDNN),generative adversarial networks (GAN), and deep Q-networks.

The learning process is a method for training a predetermined targetdevice (for example, a robot) using a plurality of learning data tocause, allow, or control the target device to make a determination orprediction. Examples of learning processes include, but are not limitedto, supervised learning, unsupervised learning, semi-supervisedlearning, or reinforcement learning.

Although the FIG. 2A shows various hardware components of the MDASserver (100) but it is to be understood that other embodiments are notlimited thereon. In other embodiments, the MDAS server (100) may includeless or more number of components. Further, the labels or names of thecomponents are used only for illustrative purpose and does not limit thescope of the invention. One or more components can be combined togetherto perform same or substantially similar function for MDAS server (100)assisted handover optimization in the wireless network (1000).

FIG. 2B illustrates a block diagram of the source gNB (200) forproviding the optimal handover in the wireless network (1000), accordingto the embodiments as disclosed herein. In an embodiment, the source gNB(200) includes a memory (210), a processor (220), a communicator (230),and a corrective action controller (240).

The memory (210) also stores instructions to be executed by theprocessor (220). The memory (210) may include non-volatile storageelements. Examples of such nonvolatile storage elements may includemagnetic hard discs, optical discs, floppy discs, flash memories, orforms of electrically programmable memories (EPROM) or electricallyerasable and programmable (EEPROM) memories. In addition, the memory(210) may, in some examples, be considered a non-transitory storagemedium. The term “non-transitory” may indicate that the storage mediumis not embodied in a carrier wave or a propagated signal. However, theterm “non-transitory” should not be interpreted that the memory (210) isnon-movable. In certain examples, a nontransitory storage medium maystore data that can, over time, change (e.g., in Random Access Memory(RAM) or cache). In an embodiment, the memory (210) can be an internalstorage unit or it can be an external storage unit of the source gNB(200), a cloud storage, or any other type of external storage.

The processor (220) communicates with the memory (210), the communicator(230), and the corrective action controller (240). The processor (220)is configured to execute instructions stored in the memory (210) and toperform various processes. The communicator (230) is configured forcommunicating internally between internal hardware components and withexternal devices via one or more networks.

In an embodiment, the corrective action controller (240) detects that aUE (400) moves towards at least one target gNB (300) from the pluralityof target gNBs in the wireless network (1000). Further, the correctiveaction controller (240) sends a request to a MDAS server (100) for theanalytical report of the at least one target gNB (300) for handover fromthe source gNB (200), where the analytical report comprises at least onea current resource consumption status for each of the target gNBs, afuture resource consumption status for each of the target gNBs, anindication on whether at least one target gNB (300) from the pluralityof target gNBs is optimal for handover at present (current optimaltarget) or at some future point of time (future optimal target), and atleast one corrective action to optimize at least one target gNB (300)for handover. Further, the corrective action controller (240) receivesthe analytical report of the at least one target gNB (300) from the MDASserver (100). Further, the corrective action controller (240) performsat least one corrective action to optimize at least one target gNB (300)for handover.

In an embodiment, the corrective action controller (240) determineswhether at least one target gNB (300) from the plurality of target gNBsis optimal for handover. Further, the corrective action controller (240)performs the handover from the source gNB (200) to the at least onetarget gNB (300) in response to determining that the at least one targetgNB (300) from the plurality of target gNBs is optimal for handover.Further, the corrective action controller (240) sends a scale-outrequest to an NFMS server (500) to increase virtual resources and radioresources associated with the at least one target gNB (300) in responseto determining that the at least one target gNB (300) from the pluralityof target gNBs is not optimal for handover.

Further, the corrective action controller (240) determines whether theUE (400) is moving away from the target gNB (300) in the wirelessnetwork (1000). Further, the corrective action controller (240) performsthe handover from the source gNB (200) to the at least one target gNB(300) in response to determining that the UE (400) is not moving awayfrom the target gNB (300) in the wireless network (1000). Further, thecorrective action controller (240) sends a scale-in request to the NFMSserver (500) to decrease virtual resources and radio resourcesassociated with the at least one target gNB (300) in response todetermining that the UE (400) is moving away from the target gNB (300)in the wireless network (1000).

Although the FIG. 2B shows various hardware components of the source gNB(200) but it is to be understood that other embodiments are not limitedthereon. In other embodiments, the source gNB (200) may include less ormore number of components. Further, the labels or names of thecomponents are used only for illustrative purpose and does not limit thescope of the invention. One or more components can be combined togetherto perform same or substantially similar function for MDAS server (100)assisted handover optimization in the wireless network (1000).

FIGS. 3A, 3B, and, 3C illustrate various operations for the MDAS server(100) assisted handover optimization in the wireless network (1000),according to the embodiments as disclosed herein.

At S302, the method includes periodically collecting, by the MDAS server(100), data from the plurality of target gNBs in the wireless network(1000). At S304, the method includes generating, by the MDAS server(100), the analytical report for each target gNB (300) of the pluralityof target gNBs based on the collected data. At S306, the method includesindicating, by the UE (400), availability of an adjacent target cell (atleast one target gNB (300)) from the source gNB (200) from the pluralityof target gNBs for handover. At S308, the method includes receiving, bythe MDAS server (100), the request for the analytical report of the atleast one target gNB (300) for handover from the source gNB (100). AtS310, the method includes sending, by the MDAS server (100), theanalytical report to the source gNB (200). At 3212, the method includesdetermining, by the source gNB (200), whether at least one target gNB(300) from the plurality of target gNBs is optimal for handover atpresent or at some future point of time.

At S314, the method includes performing, by the source gNB (200), thehandover from the source gNB (200) to the at least one target gNB (300)in response to determining that the at least one target gNB (300) fromthe plurality of target gNBs is optimal for handover. At S316, themethod includes sending, by the source gNB (200), the scale-out requestto the NFMS server (500) to increase virtual resources and radioresources associated with the at least one target gNB (300) in responseto determining that the at least one target gNB (300) from the pluralityof target gNBs is not optimal for handover. At S318, the method includesallocating, by the NFMS server (500), additional virtual resource andradio resources to the at least one target gNB (300). At S320, themethod includes informing, by the NFMS server (500), successfulscale-out of the at least one target gNB (300) to the source gNB (200).At S322, the method includes determining, by the source gNB (200),whether the UE (400) is moving away from the target gNB (300) in thewireless network (1000).

At S324, the method includes performing, by the source gNB (200), thehandover from the source gNB (200) to the at least one target gNB (300)in response to determining that the UE (400) is not moving away from thetarget gNB (300) in the wireless network (1000). At S326, the methodincludes sending, by the source gNB (200), the scale-in request to theNFMS server (500) to decrease virtual resources and radio resourcesassociated with the at least one target gNB (300) in response todetermining that the UE (400) is moving away from the target gNB (300)in the wireless network (1000). At S328, the method includesde-allocating, by the NFMS server (500), additional radio resources tothe at least one target gNB (300). At S330, the method includesinforming, by the NFMS server (500), successful scale-in of the at leastone target gNB (300) to the source gNB (200).

FIG. 4A and FIG. 4B illustrate the MDAS server (100) assisted handoveroptimization in the wireless network (1000), according to theembodiments as disclosed herein.

At 401, the MDAS server (100) collects various gNB resources relateddata periodically. The data is then analyzed to generate the analyticalreport. At 402, the UE (400) sends RAN data to the source gNB (200) andindicates availability of the adjacent target cell (e.g. target gNB(300)) to the source gNB (200) for handover. At 403, the source gNB(200) determines handover is required for the UE (400) and the sourcegNB (200) identifies the target gNB (300). At 404, the source gNB (200)sends the request for a resource analytics report for the target gNB(300) to the MDAS server (100) (i.e. MDAS producer). At 405, the MDASserver (100) identifies and prepares the related report. At 406, theMDAS server (100) provides the target gNB (300) resource analyticsreport containing an assigned virtual resource and radio resource, aconsumed virtual resource and radio resource, a projected virtual andRAN resource usage in near future, a current optimal target (i.e.YES/NO), a future optimal target (i.e. YES/NO)/future timestamp andremedial action (e.g. scale-out gNB, increase radio resource (RRCconnected users, PDCP).

At 407, when the target gNB (300) is optimal for handover, advances thehandover procedures as usual. At 408, when the target gNB (300) is notoptimal for handover, scale-out the target gNB (300). The source gNB(200) acting as NFMS consumer, sends ModifyNf request (section 7.11,3GPP TS 28.531) to NFMS producer (the NFMS server (500), the NFMS server(500) includes a NFMF (500 a), and a NFV MANO (500 b)) to scale-out thetarget gNB (300 allocating additional virtual resources to the targetgNB (300). Further, the NFMS producer confirms the successfully scaleoutof the target gNB (300) to the source gNB (200) and increase virtualresources and radio resources of the target gNB (300). At 409, thesource gNB (200) performs advances in the handover procedures as usual.

At 410, if the source gNB (200) decides not to handover (because of theUE (400) is moving away from the target gNB (200) or something else),the NFMS consumer sends ModifyNf request (section 7.11, 3GPP TS 28.531)to the NFMS producer to scale-in the target gNB (200). Further, the NFMSproducer de-allocate additional virtual resources to the target gNB(200). Further, the NFMS producer confirms the successfully scale-in ofthe target gNB (300) to source gNB (200). At 411, radio resources arereduced in proportion with the increase done.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the embodiments herein that others can, byapplying current knowledge, readily modify and/or adapt for variousapplications such specific embodiments without departing from thegeneric concept, and, therefore, such adaptations and modificationsshould and are intended to be comprehended within the meaning and rangeof equivalents of the disclosed embodiments. It is to be understood thatthe phraseology or terminology employed herein is for the purpose ofdescription and not of limitation. Therefore, while the embodimentsherein have been described in terms of preferred embodiments, thoseskilled in the art will recognize that the embodiments herein can bepracticed with modification within the spirit and scope of theembodiments as described herein.

1. A method for a Management data analytic service (MDAS) server (100)assisted handover in a wireless network (1000), the method comprises:collecting, by the MDAS server (100), data from a plurality of targetgNBs in the wireless network (1000); generating, by the MDAS server(100), an analytical report for each target gNB (300) of the pluralityof target gNBs based on the collected data, wherein the analyticalreport comprises at least one a current resource consumption status foreach of the target gNBs, a future resource consumption status for eachof the target gNBs, an indication on whether at least one target gNB(300) from the plurality of target gNBs is optimal for handover, and atleast one corrective action to optimize at least one target gNB (300)for handover; receiving, by the MDAS server (100), a request for theanalytical report of the at least one target gNB (300) for handover froma source gNB (200); and sending, by the MDAS server (100), theanalytical report to the source gNB (200).
 2. The method as claimed inclaim 1, wherein the collected data comprises a total amount of computedresource allocated to a virtual machine, a total amount of aggregatedcomputed resource consumption at a particular point, a total amount ofstorage allocated to the virtual machine, a total amount of aggregatedstorage consumption at a particular point of time, and various radioresources and percentage of overall consumption of the various radioresources.
 3. The method as claimed in claim 1, wherein the analyticalreport comprises an assigned virtual resource and radio resource of eachof the target gNBs, a consumed virtual resource and radio resource ofeach of the target gNBs, a projected virtual resource and radio resourceusage in near future for each of the target gNBs, an indication onwhether at least one target gNB (300) from the plurality of target gNBsis optimal for handover for at least one of present time and future timewith timestamp, and at least one corrective action to optimize each ofthe target gNBs for handover.
 4. The method as claimed in claim 1,further comprises: receiving, by the source gNB (200), the analyticalreport of the at least one target gNB (300) from the MDAS server (100);and performing, by the source gNB (200), the at least one correctiveaction to optimize at least one target gNB (300) for handover.
 5. Amethod for Management data analytic service (MDAS) server (100) assistedhandover in a wireless network (1000), wherein the wireless network(1000) comprises a plurality of gNBs, the method comprises: detecting,by a source gNB (200) from the plurality of gNBs, that a user equipment(UE) moves towards at least one target gNB (300) from a plurality oftarget gNBs in the wireless network (1000); sending, by the source gNB(200), a request to an MDAS server (100) for an analytical report of theat least one target gNB (300) for handover from the source gNB (200),wherein the analytical report comprises at least one a current resourceconsumption status for each of the target gNBs, a future resourceconsumption status for each of the target gNBs, an indication on whetherat least one target gNB (300) from the plurality of target gNBs isoptimal for handover, and at least one corrective action to optimize atleast one target gNB (300) for handover; receiving, by the source gNB(200), the analytical report of the at least one target gNB (300) fromthe MDAS server (100); and performing, by the source gNB (200), at leastone corrective action to optimize at least one target gNB (300) forhandover.
 6. The method as claimed in claim 5, wherein performing the atleast one corrective action to optimize at least one target gNB (300)for handover, comprises: determining, by the source gNB (200), whetherat least one target gNB (300) from the plurality of target gNBs isoptimal for handover; and performing one of: performing, by the sourcegNB (200), the handover from the source gNB (200) to the at least onetarget gNB (300) in response to determining that the at least one targetgNB (300) from the plurality of target gNBs is optimal for handover, andsending, by the source gNB (200), a scale-out request to an NFMS server(500) to increase virtual resources and radio resources associated withthe at least one target gNB (300) in response to determining that the atleast one target gNB (300) from the plurality of target gNBs is notoptimal for handover.
 7. The method as claimed in claim 6, whereinsending the scale-out request to the NFMS server (500), furthercomprises: allocating, by the NFMS server (500), additional virtualresources and radio resources to the at least one target gNB (300);informing, by the NFMS server (500), successful scale-out of the atleast one target gNB (300) to the source gNB (200); and determining, bythe source gNB (200), whether a user equipment (UE) is moving away fromthe target gNB (300) in the wireless network (1000).
 8. The method asclaimed in claim 7, wherein determining, by the source gNB (200),whether the UE (400) is moving away from the target gNB (300) in thewireless network (1000), comprises: performing, by the source gNB (200),one of: performing, by the source gNB (200), the handover from thesource gNB (200) to the at least one target gNB (300) in response todetermining that the UE (400) is not moving away from the target gNB(300) in the wireless network (1000), and sending, by the source gNB(200), a scale-in request to the NFMS server (500) to decrease virtualresources and radio resources associated with the at least one targetgNB (300) in response to determining that the UE (400) is moving awayfrom the target gNB (300) in the wireless network (1000).
 9. AManagement data analytic service (MDAS) server (100) for optimalhandover in a wireless network (1000), the MDAS server (100) comprises:a memory (110); a processor (120) coupled with the memory (110); and ananalytical report controller (140), coupled with the processor (120),configured to: collect data from a plurality of target gNBs in thewireless network (1000); generate an analytical report for each targetgNB (300) of the plurality of target gNBs based on the collected data,wherein the analytical report comprises at least one a current resourceconsumption status for each of the target gNBs, a future resourceconsumption status for each of the target gNBs, an indication on whetherat least one target gNB (300) from a plurality of target gNBs is optimalfor handover, and at least one corrective action to optimize at leastone target gNB (300) for handover; receive a request for the analyticalreport of the at least one target gNB (300) for handover from a sourcegNB (200); and send the analytical report to the source gNB (200). 10.The MDAS server (100) as claimed in claim 9, wherein the collected datacomprises a total amount of computed resource allocated to a virtualmachine, a total amount of aggregated computed resource consumption at aparticular point, a total amount of storage allocated to the virtualmachine, a total amount of aggregated storage consumption at aparticular point of time, and various radio resources and percentage ofoverall consumption of the various radio resources.
 11. The MDAS server(100) as claimed in claim 9, wherein the analytical report comprises anassigned virtual resource and radio resource of each of the target gNBs,a consumed virtual resource and radio resource of each of the gNBstarget, a projected virtual resource and radio resource usage in nearfuture for each of the target gNBs, an indication on whether at leastone target gNB (300) from the plurality of target gNBs is optimal forhandover for at least one of present and in future time stamp, and atleast one corrective action to optimize each of the target gNBs forhandover.
 12. The MDAS server (100) as claimed in claim 9, wherein theMDAS server (100) provides the analytical report describing the resourceconsumption to the source gNB (200) based on the current and futurevirtual resource consumption of the at least one target gNB (300).
 13. Asource gNB (200) for optimal handover in a wireless network (1000), thesource gNB (200) comprises: a memory (210); a processor (220) coupledwith the memory (210); and a corrective action controller (240), coupledwith the processor (220), configured to: detect that a user equipment(UE) moves towards at least one target gNB (300) from a plurality oftarget gNBs in the wireless network (1000); send a request to an MDASserver (100) for an analytical report of the at least one target gNB(300) for handover from the source gNB (200), wherein the analyticalreport comprises at least one a current resource consumption status foreach of the target gNBs, a future resource consumption status for eachof the target gNBs, an indication on whether at least one target gNB(300) from a plurality of target gNBs is optimal for handover, and atleast one corrective action to optimize at least one target gNB (300)for handover; receive the analytical report of the at least one targetgNB (300) from the MDAS server (100); and perform at least onecorrective action to optimize at least one target gNB (300) forhandover.
 14. The source gNB (200) as claimed in claim 13, whereinperform the at least one corrective action to optimize at least onetarget gNB (300) for handover, comprises: determining, by the source gNB(200), whether at least one target gNB (300) from the plurality oftarget gNBs is optimal for handover; and performing one of: performing,by the source gNB (200), the handover from the source gNB (200) to theat least one target gNB (300) in response to determining that the atleast one target gNB (300) from the plurality of target gNBs is optimalfor handover, and sending, by the source gNB (200), a scale-out requestto an NFMS server (500) to increase virtual resources and radioresources associated with the at least one target gNB (300) in responseto determining that the at least one target gNB (300) from the pluralityof target gNBs is not optimal for handover.
 15. The source gNB (200) asclaimed in claim 14, wherein sending the scale-out request to the NFMSserver (500), further comprises: allocating, by the NFMS server (500),additional virtual resources and radio resources to the at least onetarget gNB (300); informing, by the NFMS server (500), successfulscale-out of the at least one target gNB (300) to the source gNB (200);and determining, by the source gNB (200), whether a user equipment (UE)is moving away from the target gNB (300) in the wireless network (1000).